Shape and lighting interact in complex ways through the bidirectional reflectance distribution function (BRDF) to produce the variety of images around us. Shape recovery with unknown BRDF and lighting is traditionally considered hard, while their joint recovery is deemed severely ill-posed.
Camera motion cues for shape recovery have been extensively studied within the purview of multiview stereo. It is well-known from early works that the Lambertian reflectance assumed by multiview stereo has limitations. Several approaches have been proposed for shape recovery with general BRDFs, such as Helmholtz reciprocity for stereo and intensity profiles for photometric stereo.
For BRDF estimation, parametric models have a long history. Non-parametric and data-driven approaches are popular for their representation power, but require a large amount of data or rely on complex estimation whose properties are hard to characterize. Semiparametric models have also been proposed for BRDF editing and estimation.